The common approach in digital imaging today is to capture as many pixels as possible and to later compress the captured image data by digital means. These common approaches visually require to first acquire the full signal and compress it in a second stage (e.g. JPEG image compression).
“Compressed sensing” (also named “compressive sensing”) is a signal processing method which allows capturing and representing signals with a low number of measurements. The signal is then reconstructed from these measurements using an optimization process. Compressed sensing is particularly useful to capture high amount and high resolution data at low cost (e.g. low energy power, low storage space, etc.). Compressed sensing allows compressing high resolution signals (e.g. images) at the optics or sensors level. In addition, it allows fast and/or low power acquisition compared to standard compression techniques. These standard techniques are usually required to first acquire the full signal and then compress it in a second stage.
Compressed sensing thus is particularly useful for low power (e.g. wearable) devices or when very large amount of recording must be acquired and stored (e.g. ‘always on’ cameras as for life logging).
A drawback of compressed sensing is that the reconstruction process may be complex and slow. In most cases this makes it impossible to reconstruct the signal on a small (e.g., wearable) device in real time or in totality. For example, if a low power camera records continuously, the signal cannot be decoded on the device but could only be reconstructed offline on a larger computer. A full reconstruction of a very large amount of recorded images could be too costly in practice.
Although some techniques exist for reconstructing images from compressed sensing image data, it is generally desirable to provide fast alternative techniques for reconstructing images from compressed sensing image data.